dc.contributor.author |
Brouder, Sylvie M. |
|
dc.contributor.author |
Volenec, Jeffrey J. |
|
dc.contributor.author |
Murrell, Scott |
|
dc.date.accessioned |
2021-12-10T03:19:06Z |
|
dc.date.available |
2021-12-10T03:19:06Z |
|
dc.date.issued |
2021 |
|
dc.identifier.citation |
Brouder S.M., Volenec J.J., Murrell T.S. (2021) The Potassium Cycle and Its Relationship to Recommendation Development. In: Murrell T.S., Mikkelsen R.L., Sulewski G., Norton R., Thompson M.L. (eds) Improving Potassium Recommendations for Agricultural Crops. Springer, Cham. https://doi.org/10.1007/978-3-030-59197-7_1 |
en_US |
dc.identifier.isbn |
978-3-030-59197-7 |
|
dc.identifier.uri |
https://doi.org/10.1007/978-3-030-59197-7_1 |
|
dc.identifier.uri |
${sadil.baseUrl}/handle/123456789/1656 |
|
dc.description |
46 p. ; PDF |
en_US |
dc.description.abstract |
Nutrient recommendation frameworks are underpinned by scientific
understanding of how nutrients cycle within timespans relevant to management
decision-making. A trusted potassium (K) recommendation is comprehensive
enough in its components to represent important differences in biophysical and
socioeconomic contexts but simple and transparent enough for logical, practical
use. Here we examine a novel six soil-pool representation of the K cycle and explore
the extent to which existing recommendation frameworks represent key plant, soil,
input, and loss pools and the flux processes among these pools. Past limitations
identified include inconsistent use of terminology, misperceptions of the universal
importance and broad application of a single soil testing diagnostic, and insufficient
correlation/calibration research to robustly characterize the probability and magnitude
of crop response to fertilizer additions across agroecozones. Important opportunities
to advance K fertility science range from developing a better understanding
of the mode of action of diagnostics through use in multivariate field trials to the use
of mechanistic models and systematic reviews to rigorously synthesize disparate
field studies and identify knowledge gaps and/or novel targets for diagnostic development.
Finally, advancing evidence-based K management requires better use of
legacy and newly collected data and harnessing emerging data science tools and
e-infrastructure to expand global collaborations and accelerate innovation. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Springer Cham |
en_US |
dc.title |
The Potassium Cycle and Its Relationship to Recommendation Development |
en_US |
dc.type |
Book chapter |
en_US |